Training AED models requires strongly labeled datasets in which onset and offset times are labeled for each sound event. Because of the laborious effort required to annotate real-world samples, researchers often use synthetically generated datasets. This requires mixing events from a curated sound bank with a background signal to synthesize soundscapes with multiple, possibly overlapping, events. For example, URBAN-SED [1]} is composed of sound events from the URBAN-8K [2]} dataset mixed with Brownian noise. A recent trend is to use a combination of synthetic and real recordings for training and evaluation as with DESED [3]}. For both datasets, however, events are distributed uniformly during soundscape generation. For FL, events should be distributed in a structured fashion to simulate real-world distributed learning conditions. To enable research in FL for AED, our proposed datasets distribute soundscapes to clients with different background characteristics and class distributions.
